Tuesday, March 07. 2017

Note: I recently found out about this curious rosettacode.org projects that presents brief solutions of the same task in "as many languages as possible" (rem.: programming languages in this case). Therefore this name, Rosetta Code. Pointing of course to the Rosetta stone that was key to understand hieroglyphs.

The project presents itself as a "programming chrestomathy" site and counts 648 programing languages so far! (839 tasks done... and counting). Babelian (programming) task ... that could possibly help restore old coded pieces.

Rosetta Code is a programming chrestomathy site. The idea is to present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another. Rosetta Code currently has 839 tasks, 202 draft tasks, and is aware of 648 languages, though we do not (and cannot) have solutions to every task in every language.

Monday, February 06. 2017

Note: following the two previous posts about algorythms and bots ("how do they ... ?), here comes a third one.

Slighty different and not really dedicated to bots per se, but which could be considered as related to "machinic intelligence" nonetheless. This time it concerns techniques and algoritms developed to understand the brain (BRAIN initiative, or in Europe the competing Blue Brain Project).

In a funny reversal, scientists applied techniques and algorythms developed to track human intelligence patterns based on data sets to the computer itself. How do a simple chip "compute information"? And the results are surprising: the computer doesn't understand how the computer "thinks" (or rather works in this case)!

This to confirm that the brain is certainly not a computer (made out of flesh)...

When you apply tools used to analyze the human brain to a computer chip that plays Donkey Kong, can they reveal how the hardware works?

Many research schemes, such as the U.S. government’s BRAIN initiative, are seeking to build huge and detailed data sets that describe how cells and neural circuits are assembled. The hope is that using algorithms to analyze the data will help scientists understand how the brain works.

But those kind of data sets don’t yet exist. So Eric Jonas of the University of California, Berkeley, and Konrad Kording from the Rehabilitation Institute of Chicago and Northwestern University wondered if they could use their analytical software to work out how a simpler system worked.

They settled on the iconic MOS 6502 microchip, which was found inside the Apple I, the Commodore 64, and the Atari Video Game System. Unlike the brain, this slab of silicon is built by humans and fully understood, down to the last transistor.

The researchers wanted to see how accurately their software could describe its activity. Their idea: have the chip run different games—including Donkey Kong, Space Invaders, and Pitfall, which have already been mastered by some AIs—and capture the behavior of every single transistor as it did so (creating about 1.5 GB per second of data in the process). Then they would turn their analytical tools loose on the data to see if they could explain how the microchip actually works.

For instance, they used algorithms that could probe the structure of the chip—essentially the electronic equivalent of a connectome of the brain—to establish the function of each area. While the analysis could determine that different transistors played different roles, the researchers write in PLOS Computational Biology, the results “still cannot get anywhere near an understanding of the way the processor really works.”

Elsewhere, Jonas and Kording removed a transistor from the microchip to find out what happened to the game it was running—analogous to so-called lesion studies where behavior is compared before and after the removal of part of the brain. While the removal of some transistors stopped the game from running, the analysis was unable to explain why that was the case.

In these and other analyses, the approaches provided interesting results—but not enough detail to confidently describe how the microchip worked. “While some of the results give interesting hints as to what might be going on,” explains Jonas, “the gulf between what constitutes ‘real understanding’ of the processor and what we can discover with these techniques was surprising.”

It’s worth noting that chips and brains are rather different: synapses work differently from logic gates, for instance, and the brain doesn’t distinguish between software and hardware like a computer. Still, the results do, according to the researchers, highlight some considerations for establishing brain understanding from huge, detailed data sets.

First, simply amassing a handful of high-quality data sets of the brains may not be enough for us to make sense of neural processes. Second, without many detailed data sets to analyze just yet, neuroscientists ought to remain aware that their tools may provide results that don’t fully describe the brain’s function.

As for the question of whether neuroscience can explain how an Atari works? At the moment, not really.

Thursday, January 26. 2017

Note: I just read this piece of news last day about Echo (Amazon's "robot assistant"), who accidentally attempted to buy large amount of toys by (always) listening and misunderstanding a phrase being told on TV by a presenter (and therefore captured by Echo in the living room and so on)... It is so "stupid" (I mean, we can see how the act of buying linked to these so-called "A.I"s is automatized by default configuration), but revealing of the kind of feedback loops that can happen with automatized decision delegated to bots and machines.

It's nothing new for voice-activated devices to behave badly when they misinterpret dialogue -- just ask anyone watching a Microsoft gaming event with a Kinect-equipped Xbox One nearby. However, Amazon's Echo devices is causing more of that chaos than usual. It started when a 6-year-old Dallas girl inadvertently ordered cookies and a dollhouse from Amazon by saying what she wanted. It was a costly goof ($170), but nothing too special by itself. However, the response to that story sent things over the top. When San Diego's CW6 discussed the snafu on a morning TV show, one of the hosts made the mistake of saying that he liked when the girl said "Alexa ordered me a dollhouse." You can probably guess what happened next.

Sure enough, the channel received multiple reports from viewers whose Echo devices tried to order dollhouses when they heard the TV broadcast. It's not clear that any of the purchases went through, but it no doubt caused some panic among people who weren't planning to buy toys that day.

It's easy to avoid this if you're worried: you can require a PIN code to make purchases through the Echo or turn off ordering altogether. You can also change the wake word so that TV personalities won't set off your speaker in the first place. However, this comedy of errors also suggests that there's a lot of work to be done on smart speakers before they're truly trustworthy. They may need to disable purchases by default, for example, and learn to recognize individual voices so that they won't respond to everyone who says the magic words. Until then, you may see repeats in the future.

Tuesday, July 05. 2016

Note: in the continuity of my previous post/documentation concerning the project Platform of Future-Past (fabric | ch's recent winning competition proposal), I publish additional images (several) and explanations about the second phase of the Platform project, for which we were mandated by Canton de Vaud (SiPAL).

The first part of this article gives complementary explanations about the project, but I also take the opportunity to post related works and researches we've done in parallel about particular implications of the platform proposal. This will hopefully bring a neater understanding to the way we try to combine experimentations-exhibitions, the creation of "tools" and the design of larger proposals in our open and process of work.

Notably, these related works concerned the approach to data, the breaking of the environment into computable elements and the inevitable questions raised by their uses as part of a public architecture project.

The information pavilion was potentially a slow, analog and digital "shape/experience shifter", as it was planned to be built in several succeeding steps over the years and possibly "reconfigure" to sense and look at its transforming surroundings.

The pavilion conserved therefore an unfinished flavour as part of its DNA, inspired by these old kind of meshed constructions (bamboo scaffoldings), almost sketched. This principle of construction was used to help "shift" if/when necessary.

In a general sense, the pavilion answered the conventional public program of an observation deck about a construction site. It also served the purpose of documenting the ongoing building process that often comes along. By doing so, we turned the "monitoring dimension" (production of data) of such a program into a base element of our proposal. That's where a former experimental installation helped us: Heterochrony.

As it can be noticed, the word "Public" was added to the title of the project between the two phases, to become Public Platform of Future-Past (PPoFP) ... which we believe was important to add. This because it was envisioned that the PPoFP would monitor and use environmental data concerning the direct surroundings of the information pavilion (but NO DATA about uses/users). Data that we stated in this case Public, while the treatment of the monitored data would also become part of the project, "architectural" (more below about it).

For these monitored data to stay public, so as for the space of the pavilion itself that would be part of the public domain and physically extends it, we had to ensure that these data wouldn't be used by a third party private service. We were in need to keep an eye on the algorithms that would treat the spatial data. Or best, write them according to our design goals (more about it below).

The Public Platform of Future-Past is a structure (an information and sightseeing pavilion), a Platform that overlooks an existing Public site while basically taking it as it is, in a similar way to an archeological platform over an excavation site.

The asphalt ground floor remains virtually untouched, with traces of former uses kept as they are, some quite old (a train platform linked to an early XXth century locomotives hall), some less (painted parking spaces). The surrounding environment will move and change consideralby over the years while new constructions will go on. The pavilion will monitor and document these changes. Therefore the last part of its name: "Future-Past".

By nonetheless touching the site in a few points, the pavilion slightly reorganizes the area and triggers spaces for a small new outdoor cafe and a bikes parking area. This enhanced ground floor program can work by itself, seperated from the upper floors.

Several areas are linked to monitoring activities (input devices) and/or displays (in red, top -- that concern interests points and views from the platform or elsewhere --). These areas consist in localized devices on the platform itself (5 locations), satellite ones directly implented in the three construction sites or even in distant cities of the larger political area --these are rather output devices-- concerned by the new constructions (three museums, two new large public squares, a new railway station and a new metro). Inspired by the prior similar installation in a public park during a festival -- Heterochrony (bottom image) --, these raw data can be of different nature: visual, audio, integers from sensors (%, °C, ppm, db, lm, mb, etc.), ...

Input and output devices remain low-cost and simple in their expression: several input devices / sensors are placed outside of the pavilion in the structural elements and point toward areas of interest (construction sites or more specific parts of them). Directly in relation with these sensors and the sightseeing spots but on the inside are placed output devices with their recognizable blue screens. These are mainly voice interfaces: voice outputs driven by one bot according to architectural "scores" or algorithmic rules (middle image). Once the rules designed, the "architectural system" runs on its own. That's why we've also named the system based on automated bots "Ar.I." It could stand for "Architectural Intelligence", as it is entirely part of the architectural project.

The coding of the "Ar.I." and use of data has the potential to easily become something more experimental, transformative and performative along the life of PPoFT.

Observers (users) and their natural "curiosity" play a central role: preliminary observations and monitorings are indeed the ones produced in an analog way by them (eyes and ears), in each of the 5 interesting points and through their wanderings. Extending this natural interest is a simple cord in front of each "output device" that they can pull on, which will then trigger a set of new measures by all the related sensors on the outside. This set new data enter the database and become readable by the "Ar.I."

The whole part of the project regarding interaction and data treatments has been subject to a dedicated short study (a document about this study can be accessed here --in French only--). The main design implications of it are that the "Ar.I." takes part in the process of "filtering" which happens between the "outside" and the "inside", by taking part to the creation of a variable but specific "inside atmosphere" ("artificial artificial", as the outside is artificial as well since the anthropocene, isn't it ?) By doing so, the "Ar.I." bot fully takes its own part to the architecture main program: triggering the perception of an inside, proposing patterns of occupations.

"Ar.I." computes spatial elements and mixes times. It can organize configurations for the pavilion (data, displays, recorded sounds, lightings, clocks). It can set it to a past, a present, but also a future estimated disposition. "Ar.I." is mainly a set of open rules and a vocal interface, at the exception of the common access and conference space equipped with visual displays as well. "Ar.I." simply spells data at some times while at other, more intriguingly, it starts give "spatial advices" about the environment data configuration.

In parallel to Public Platform of Future Past and in the frame of various research or experimental projects, scientists and designers at fabric | ch have been working to set up their own platform for declaring and retrieving data (more about this project, Datadroppers, here). A platform, simple but that is adequate to our needs, on which we can develop as desired and where we know what is happening to the data. To further guarantee the nature of the project, a "data commune" was created out of it and we plan to further release the code on Github.

In tis context, we are turning as well our own office into a test tube for various monitoring systems, so that we can assess the reliability and handling of different systems. It is then the occasion to further "hack" some basic domestic equipments and turn them into sensors, try new functions as well, with the help of our 3d printer in tis case (middle image). Again, this experimental activity is turned into a side project, Studio Station (ongoing, with Pierre-Xavier Puissant), while keeping the general background goal of "concept-proofing" the different elements of the main project.

A common room (conference room) in the pavilion hosts and displays the various data. 5 small screen devices, 5 voice interfaces controlled for the 5 areas of interests and a semi-transparent data screen. Inspired again by what was experimented and realized back in 2012 during Heterochrony (top image).

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PPoFP, several images. Day, night configurations & few comments

Public Platform of Future-Past, axonometric views day/night.

An elevated walkway that overlook the almost archeological site (past-present-future). The circulations and views define and articulate the architecture and the five main "points of interests". These mains points concentrates spatial events, infrastructures and monitoring technologies. Layer by layer, the suroundings are getting filtrated by various means and become enclosed spaces.

Friday, May 27. 2016

Note: "(...) For example, technologists might be held responsible if they use poor quality data to train AI systems, or fossilize prejudices based on race, age, or gender into the algorithms they design."

An important problem that I can see for designers and architects is that if you don't agree with the principles --commercial, social, ethical and almost conceptual-- implied by the technologies (i.e. any "homekit" like platforms controlled by bots), you won't find many if any counter propositions/techs to work with (all large diffusion products will support iOS, Android and the likes). It is almost a dictatorship of products hidden behind a "participate" paradigma. Either you'll be in and accept the conditions (you might use an API provided with the service --FB, Twitter, IFTTT, Apple, Google, Wolfram, Siemens, MS, etc.--, but then feed the central company nonetheless), or out... or possibly develop you own solution(s) that will probably be a pain in the ass to use for your client because it/they will clearly be side products hard to maintain, update, etc.

"Some" open source projects driven by "some" communities could be/become (should be) alternative solutions of course, but for now these are good for prototyping and teaching, not for consistent "domestic" applications... And when they'll possibly do so, they might likely be bought. So we'll have "difficulties" as (interaction) designers, so to say: you'll work for your client(s) ... and the corp. that provides the services you'll use!

Should the government regulate artificial intelligence? That was the central question of the first White House workshop on the legal and governance implications of AI, held in Seattle on Tuesday.

“We are observing issues around AI and machine learning popping up all over the government,” said Ed Felten, White House deputy chief technology officer. “We are nowhere near the point of broadly regulating AI … but the challenge is how to ensure AI remains safe, controllable, and predictable as it gets smarter.”

One of the key aims of the workshop, said one of its organizers, University of Washington law professor Ryan Calo, was to help the public understand where the technology is now and where it’s headed. “The idea is not for the government to step in and regulate AI but rather to use its many other levers, like coördination among the agencies and procurement power,” he said. Attendees included technology entrepreneurs, academics, and members of the public.

In a keynote speech, Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, noted that we are still in the Dark Ages of machine learning, with AI systems that generally only work well on well-structured problems like board games and highway driving. He championed a collaborative approach where AI can help humans to become safer and more efficient. “Hospital errors are the third-leading cause of death in the U.S.,” he said. “AI can help here. Every year, people are dying because we’re not using AI properly in hospitals.”

Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, left, speaks with attendees at the White House workshop on artificial intelligence.

Nevertheless, Etzioni considers it far too early to talk about regulating AI: “Deep learning is still 99 percent human work and human ingenuity. ‘My robot did it’ is not an excuse. We have to take responsibility for what our robots, AI, and algorithms do.”

A panel on “artificial wisdom” focused on when these human-AI interactions go wrong, such as the case of an algorithm designed to predict future criminal offenders that appears to be racially biased. “The problem is not about the AI agents themselves, it’s about humans using technological tools to oppress other humans in finance, criminal justice, and education,” said Jack Balkin of Yale Law School.

Several academics supported the idea of an “information fiduciary”: giving people who collect big data and use AI the legal duties of good faith and trustworthiness. For example, technologists might be held responsible if they use poor quality data to train AI systems, or fossilize prejudices based on race, age, or gender into the algorithms they design.

As government institutions increasingly rely on AI systems for decision making, those institutions will need personnel who understand the limitations and biases inherent in data and AI technology, noted Kate Crawford, a social scientist at Microsoft Research. She suggested that students be taught ethics alongside programming skills.

Bryant Walker Smith from the University of South Carolina proposed regulatory flexibility for rapidly evolving technologies, such as driverless cars. “Individual companies should make a public case for the safety of their autonomous vehicles,” he said. “They should establish measures and then monitor them over the lifetime of their systems. We need a diversity of approaches to inform public debate.”

This was the first of four workshops planned for the coming months. Two will address AI for social good and issues around safety and control, while the last will dig deeper into the technology’s social and economic implications. Felten also announced that the White House would shortly issue a request for information to give the general public an opportunity to weigh in on the future of AI.

The elephant in the room, of course, was November’s presidential election. In a blog post earlier this month, Felten unveiled a new National Science and Technology Council Subcommittee on Machine Learning and Artificial Intelligence, focused on using AI to improve government services “between now and the end of the Administration.”

Tuesday, May 24. 2016

Note: even people developing automation will be automated, so to say...

Do you want to change this existing (and predictable) future? This would be the right time to come with counter-proposals then...

But I'm quite surprized by the absence of nuanced analysis in the Wired article btw (am I? further than "make the workd a better place" I mean): indeed, this is a scientific achievement, but then what? no stakes? no social issues? It seems to be the way things should go then... (and some people know pretty well how "The Way Things Go", always wrong ;)), to the point that " No, Asimo isn’t quite as advanced—or as frightening—as Skynet." Good to know!

Deep neural networks are remaking the Internet. Able to learn very human tasks by analyzing vast amounts of digital data, these artificially intelligent systems are injecting online services with a power that just wasn’t viable in years past. They’re identifying faces in photos and recognizing commands spoken into smartphones and translating conversations from one language to another. They’re even helping Google choose its search results. All this we know. But what’s less discussed is how the giants of the Internet go about building these rather remarkable engines of AI.

Part of it is that companies like Google and Facebook pay top dollar for some really smart people. Only a few hundred souls on Earth have the talent and the training needed to really push the state-of-the-art forward, and paying for these top minds is a lot like paying for an NFL quarterback. That’s a bottleneck in the continued progress of artificial intelligence. And it’s not the only one. Even the top researchers can’t build these services without trial and error on an enormous scale. To build a deep neural network that cracks the next big AI problem, researchers must first try countless options that don’t work, running each one across dozens and potentially hundreds of machines.

“It’s almost like being the coach rather than the player,” says Demis Hassabis, co-founder of DeepMind, the Google outfit behind the history-making AI that beat the world’s best Go player. “You’re coaxing these things, rather than directly telling them what to do.”

That’s why many of these companies are now trying to automate this trial and error—or at least part of it. If you automate some of the heavily lifting, the thinking goes, you can more rapidly push the latest machine learning into the hands of rank-and-file engineers—and you can give the top minds more time to focus on bigger ideas and tougher problems. This, in turn, will accelerate the progress of AI inside the Internet apps and services that you and I use every day.

In other words, for computers to get smarter faster, computers themselves must handle even more of the grunt work. The giants of the Internet are building computing systems that can test countless machine learning algorithms on behalf of their engineers, that can cycle through so many possibilities on their own. Better yet, these companies are building AI algorithms that can help build AI algorithms. No joke. Inside Facebook, engineers have designed what they like to call an “automated machine learning engineer,” an artificially intelligent system that helps create artificially intelligent systems. It’s a long way from perfection. But the goal is to create new AI models using as little human grunt work as possible.

Feeling the Flow

After Facebook’s $104 billion IPO in 2012, Hussein Mehanna and other engineers on the Facebook ads team felt an added pressure to improve the company’s ad targeting, to more precisely match ads to the hundreds of millions of people using its social network. This meant building deep neural networks and other machine learning algorithms that could make better use of the vast amounts of data Facebook collects on the characteristics and behavior of those hundreds of millions of people.

According to Mehanna, Facebook engineers had no problem generating ideas for new AI, but testing these ideas was another matter. So he and his team built a tool called Flow. “We wanted to build a machine-learning assembly line that all engineers at Facebook could use,” Mehanna says. Flow is designed to help engineers build, test, and execute machine learning algorithms on a massive scale, and this includes practically any form of machine learning—a broad technology that covers all services capable of learning tasks largely on their own.

Basically, engineers could readily test an endless stream of ideas across the company’s sprawling network of computer data centers. They could run all sorts of algorithmic possibilities—involving not just deep learning but other forms of AI, including logistic regression to boosted decision trees—and the results could feed still more ideas. “The more ideas you try, the better,” Mehanna says. “The more data you try, the better.” It also meant that engineers could readily reuse algorithms that others had built, tweaking these algorithms and applying them to other tasks.

With Flow, Mehanna says, Facebook trains and tests about 300,000 machine learning models each month. Whereas it once rolled a new AI model onto its social network every 60 days or so, it can now release several new models each week.

The Next Frontier

The idea is far bigger than Facebook. It’s common practice across the world of deep learning. Last year, Twitter acquired a startup, WhetLab, that specializes in this kind of thing, and recently, Microsoft described how its researchers use a system to test a sea of possible AI models. Microsoft researcher Jian Sun calls it “human-assisted search.”

Mehanna and Facebook want to accelerate this. The company plans to eventually open source Flow, sharing it with the world at large, and according to Mehanna, outfits like LinkedIn, Uber, and Twitter are already interested in using it. Mehanna and team have also built a tool called AutoML that can remove even more of the burden from human engineers. Running atop Flow, AutoML can automatically “clean” the data needed to train neural networks and other machine learning algorithms—prepare it for testing without any human intervention—and Mehanna envisions a version that could even gather the data on its own. But more intriguingly, AutoML uses artificial intelligence to help build artificial intelligence.

As Mehana says, Facebook trains and tests about 300,000 machine learning models each month. AutoML can then use the results of these tests to train another machine learning model that can optimize the training of machine learning models. Yes, that can be a hard thing to wrap your head around. Mehanna compares it to Inception. But it works. The system can automatically chooses algorithms and parameters that are likely to work. “It can almost predict the result before the training,” Mehanna says.

Inside the Facebook ads team, engineers even built that automated machine learning engineer, and this too has spread to the rest of the company. It’s called Asimo, and according to Facebook, there are cases where it can automatically generate enhanced and improved incarnations of existing models—models that human engineers can then instantly deploy to the net. “It cannot yet invent a new AI algorithm,” Mehanna says. “But who knows, down the road…”

It’s an intriguing idea—indeed, one that has captivated science fiction writers for decades: an intelligent machine that builds itself. No, Asimo isn’t quite as advanced—or as frightening—as Skynet. But it’s a step toward a world where so many others, not just the field’s sharpest minds, will build new AI. Some of those others won’t even be human.

More generally, thinking the Future in different terms than liberalism is an absolute necessity. Especially in a context where, also as stated, "Automation and unemployment are the future, regardless of any human intervention".

IN THE NEXT FEW DECADES, your job is likely to be automated out of existence. If things keep going at this pace, it will be great news for capitalism. You’ll join the floating global surplus population, used as a threat and cudgel against those “lucky” enough to still be working in one of the few increasingly low-paying roles requiring human input. Existing racial and geographical disparities in standards of living will intensify as high-skill, high-wage, low-control jobs become more rarified and centralized, while the global financial class shrinks and consolidates its power. National borders will continue to be used to control the flow of populations and place migrant workers outside of the law. The environment will continue to be the object of vicious extraction and the dumping ground for the negative externalities of capitalist modes of production.

It doesn’t have to be this way, though. While neoliberal capitalism has been remarkably successful at laying claim to the future, it used to belong to the left — to the party of utopia. Nick Srnicek and Alex Williams’s Inventing the Future argues that the contemporary left must revive its historically central mission of imaginative engagement with futurity. It must refuse the all-too-easy trap of dismissing visions of technological and social progress as neoliberal fantasies. It must seize the contemporary moment of increasing technological sophistication to demand a post-scarcity future where people are no longer obliged to be workers; where production and distribution are democratically delegated to a largely automated infrastructure; where people are free to fish in the afternoon and criticize after dinner. It must combine a utopian imagination with the patient organizational work necessary to wrest the future from the clutches of hegemonic neoliberalism.

Strategies and Tactics

In making such claims, Srnicek and Williams are definitely preaching to the leftist choir, rather than trying to convert the masses. However, this choir is not just the audience for, but also the object of, their most vituperative criticism. Indeed, they spend a great deal of the book arguing that the contemporary left has abandoned strategy, universalism, abstraction, and the hard work of building workable, global alternatives to capitalism. Somewhat condescendingly, they group together the highly variegated field of contemporary leftist tactics and organizational forms under the rubric of “folk politics,” which they argue characterizes a commitment to local, horizontal, and immediate actions. The essentially affective, gestural, and experimental politics of movements such as Occupy, for them, are a retreat from the tradition of serious militant politics, to something like “politics-as-drug-experience.”

Whatever their problems with the psychodynamics of such actions, Srnicek and Williams argue convincingly that localism and small-scale, prefigurative politics are simply inadequate to challenging the ideological dominance of neoliberalism — they are out of step with the actualities of the global capitalist system. While they admire the contemporary left’s commitment to self-interrogation, and its micropolitical dedication to the “complete removal of all forms of oppression,” Srnicek and Williams are ultimately neo-Marxists, committed to the view that “[t]he reality of complex, globalised capitalism is that small interventions consisting of relatively non-scalable actions are highly unlikely to ever be able to reorganise our socioeconomic system.” The antidote to this slow localism, however, is decidedly not fast revolution.

Instead, Inventing the Future insists that the left must learn from the strategies that ushered in the currently ascendant neoliberal hegemony. Inventing the Future doesn’t spend a great deal of time luxuriating in pathos, preferring to learn from their enemies’ successes rather than lament their excesses. Indeed, the most empirically interesting chunk of their book is its careful chronicle of the gradual, stepwise movement of neoliberalism from the “fringe theory” of a small group of radicals to the dominant ideological consensus of contemporary capitalism. They trace the roots of the “neoliberal thought collective” to a diverse range of trends in pre–World War II economic thought, which came together in the establishment of a broad publishing and advocacy network in the 1950s, with the explicit strategic aim of winning the hearts and minds of economists, politicians, and journalists. Ultimately, this strategy paid off in the bloodless neoliberal revolutions during the international crises of Keynesianism that emerged in the 1980s.

What made these putsches successful was not just the neoliberal thought collective’s ability to represent political centrism, rational universalism, and scientific abstraction, but also its commitment to organizational hierarchy, internal secrecy, strategic planning, and the establishment of an infrastructure for ideological diffusion. Indeed, the former is in large part an effect of the latter: by the 1980s, neoliberals had already spent decades engaged in the “long-term redefinition of the possible,” ensuring that the institutional and ideological architecture of neoliberalism was already well in place when the economic crises opened the space for swift, expedient action.

Demands

Srnicek and Williams argue that the left must abandon its naïve-Marxist hopes that, somehow, crisis itself will provide the space for direct action to seize the hegemonic position. Instead, it must learn to play the long game as well. It must concentrate on building institutional frameworks and strategic vision, cultivating its own populist universalism to oppose the elite universalism of neoliberal capital. It must also abandon, in so doing, its fear of organizational closure, hierarchy, and rationality, learning instead to embrace them as critical tactical components of universal politics.

There’s nothing particularly new about Srnicek and Williams’s analysis here, however new the problems they identify with the collapse of the left into particularism and localism may be. For the most part, in their vituperations, they are acting as rather straightforward, if somewhat vernacular, followers of the Italian politician and Marxist theorist Antonio Gramsci. As was Gramsci’s, their political vision is one of slow, organizationally sophisticated, passive revolution against the ideological, political, and economic hegemony of capitalism. The gradual war against neoliberalism they envision involves critique and direct action, but will ultimately be won by the establishment of a post-work counterhegemony.

In putting forward their vision of this organization, they strive to articulate demands that would allow for the integration of a wide range of leftist orientations under one populist framework. Most explicitly, they call for the automation of production and the provision of a basic universal income that would provide each person the opportunity to decide how they want to spend their free time: in short, they are calling for the end of work, and for the ideological architecture that supports it. This demand is both utopian and practical; they more or less convincingly argue that a populist, anti-work, pro-automation platform might allow feminist, antiracist, anticapitalist, environmental, anarchist, and postcolonial struggles to become organized together and reinforce one another. Their demands are universal, but designed to reflect a rational universalism that “integrates difference rather than erasing it.” The universal struggle for the future is a struggle for and around “an empty placeholder that is impossible to fill definitively” or finally: the beginning, not the end, of a conversation.

In demanding full automation of production and a universal basic income, Srnicek and Williams are not being millenarian, not calling for a complete rupture with the present, for a complete dismantling and reconfiguration of contemporary political economy. On the contrary, they argue that “it is imperative […] that [the left’s] vision of a new future be grounded upon actually existing tendencies.” Automation and unemployment are the future, regardless of any human intervention; the momentum may be too great to stop the train, but they argue that we can change tracks, can change the meaning of a future without work. In demanding something like fully automated luxury communism, Srnicek and Williams are ultimately asserting the rights of humanity as a whole to share in the spoils of capitalism.

Criticisms

Inventing the Future emerged to a relatively high level of fanfare from leftist social media. Given the publicity, it is unsurprising that other more “engagé” readers have already advanced trenchant and substantive critiques of the future imagined by Srnicek and Williams. More than a few of these critics have pointed out that, despite their repeated insistence that their post-work future is an ecologically sound one, Srnicek and Williams evince roughly zero self-reflection with respect either to the imbrication of microelectronics with brutally extractive regimes of production, or to their own decidedly antiquated, doctrinaire Marxist understanding of humanity’s relationship towards the nonhuman world. Similarly, the question of what the future might mean in the Anthropocene goes largely unexamined.

More damningly, however, others have pointed out that despite the acknowledged counterintuitiveness of their insistence that we must reclaim European universalism against the proliferation of leftist particularisms, their discussions of postcolonial struggle and critique are incredibly shallow. They are keen to insist that their universalism will embrace rather than flatten difference, that it will be somehow less brutal and oppressive than other forms of European univeralism, but do little of the hard argumentative work necessary to support these claims. While we see the start of an answer in their assertion that the rejection of universal access to discourses of science, progress, and rationality might actually function to cement certain subject-positions’ particularity, this — unfortunately — remains only an assertion. At best, they are being uncharitable to potential allies in refusing to take their arguments seriously; at worst, they are unreflexively replicating the form if not the content of patriarchal, racist, and neocolonial capitalist rationality.

For my part, while I find their aggressive and unapologetic presentation of their universalism somewhat off-putting, their project is somewhat harder to criticize than their book — especially as someone acutely aware of the need for more serious forms of organized thinking about the future if we’re trying to push beyond the horizons offered by the neoliberal consensus.

However, as an anthropologist of the computer and data sciences, it’s hard for me to ignore a curious and rather serious lacuna in their thinking about automaticity, algorithms, and computation. Beyond the automation of work itself, they are keen to argue that with contemporary advances in machine intelligence, the time has come to revisit the planned economy. However, in so doing, they curiously seem to ignore how this form of planning threatens to hive off economic activity from political intervention. Instead of fearing a repeat of the privations that poor planning produced in earlier decades, the left should be more concerned with the forms of control and dispossession successful planning produced. The past decade has seen a wealth of social-theoretical research into contemporary forms of algorithmic rationality and control, which has rather convincingly demonstrated the inescapable partiality of such systems and their tendency to be employed as decidedly undemocratic forms of technocratic management.

Srnicek and Williams, however, seem more or less unaware of, or perhaps uninterested in, such research. At the very least, they are extremely overoptimistic about the democratization and diffusion of expertise that would be required for informed mass control over an economy planned by machine intelligence. I agree with their assertion that “any future left must be as technically fluent as it is politically fluent.” However, their definition of technical fluency is exceptionally narrow, confined to an understanding of the affordances and internal dynamics of technical systems rather than a comprehensive analysis of their ramifications within other social structures and processes. I do not mean to suggest that the democratic application of machine learning and complex systems management is somehow a priori impossible, but rather that Srnicek and Williams do not even seem to see how such systems might pose a challenge to human control over the means of production.

In a very real sense, though, my criticisms should be viewed as a part of the very project proposed in the book. Inventing the Future is unapologetically a manifesto, and a much-overdue clarion call to a seriously disorganized metropolitan left to get its shit together, to start thinking — and arguing — seriously about what is to be done. Manifestos, like demands, need to be pointed enough to inspire, while being vague enough to promote dialogue, argument, dissent, and ultimately action. It’s a hard tightrope to walk, and Srnicek and Williams are not always successful. However, Inventing the Future points towards an altogether more coherent and mature project than does their #ACCELERATE MANIFESTO. It is hard to deny the persuasiveness with which the book puts forward the positive contents of a new and vigorous populism; in demanding full automation and universal basic income from the world system, they also demand the return of utopian thinking and serious organization from the left.

Wednesday, December 23. 2015

Note: ... and while I'm talking about the gallery Circuit in Lausanne, don't forget that there is still Kazuko Miyamoto's exhibition going on for a few more weeks. For the record, Ms Miyamoto was one of the first assistant of Sol Lewitt and was involved on his early wall drawings. Their collaboration lasted for almost three decades. She was also founder of the A.I.R. Gallery in NYC.

Her works are undoubtedly related to the ones of Lewitt as one can feel the rules behind them. Yet they are probably more fragile and aerial. Literally for many of them, as they are made out of yarn strings...

fabric | rblg

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